Line-based rational function model for high-resolution satellite imagery

Tee-Ann Teo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The object-to-image transformation of high-resolution satellite images often involves a rational functional model (RFM). Traditionally, RFM uses point features to obtain the transformation coefficients. Since control lines offer greater flexibility than control points, this study proposes a new RFM approach based on linear features. The proposed methods include direct RFM and bias-compensated RFM using control lines. The former obtains the rational polynomial coefficients (RPCs) directly from control lines, whereas the latter uses sensor-orientated RPCs and control lines to determine compensated coefficients. The line-based RFMs include vector and parametric line representations. The experiments in this study analysed the effects of line number, orientation, and length using simulation and real data. The real data combined three-dimensional building models and high-resolution satellite images, such as IKONOS and QuickBird images. Experimental results show that the proposed algorithms can achieve pixel-level accuracy.

Original languageEnglish
Pages (from-to)1355-1372
Number of pages18
JournalInternational Journal of Remote Sensing
Volume34
Issue number4
DOIs
StatePublished - 1 Jan 2013

Fingerprint

Dive into the research topics of 'Line-based rational function model for high-resolution satellite imagery'. Together they form a unique fingerprint.

Cite this